Overview

Dataset statistics

Number of variables22
Number of observations7405
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory184.0 B

Variable types

Numeric20
Categorical2

Alerts

IMPOSTO SOBRE EXPORTAÇÃO is highly skewed (γ1 = 43.95221363)Skewed
UF is uniformly distributedUniform
IRPF has unique valuesUnique
IRPJ - DEMAIS EMPRESAS has unique valuesUnique
IRRF - RENDIMENTOS DO TRABALHO has unique valuesUnique
TOTAL_ARRECADADO has unique valuesUnique
IMPOSTO SOBRE EXPORTAÇÃO has 767 (10.4%) zerosZeros
IPI - FUMO has 2943 (39.7%) zerosZeros
IPI - BEBIDAS has 604 (8.2%) zerosZeros
IPI - AUTOMÓVEIS has 3073 (41.5%) zerosZeros
IPI - VINCULADO À IMPORTACAO has 524 (7.1%) zerosZeros
IRPJ - ENTIDADES FINANCEIRAS has 614 (8.3%) zerosZeros
IRRF - REMESSAS P/ EXTERIOR has 167 (2.3%) zerosZeros
CPMF has 3904 (52.7%) zerosZeros
COFINS has 6541 (88.3%) zerosZeros

Reproduction

Analysis started2024-06-24 15:16:30.556459
Analysis finished2024-06-24 15:16:48.501854
Duration17.95 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Ano
Real number (ℝ)

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.2258
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2024-06-24T12:16:48.538033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12007
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7673629
Coefficient of variation (CV)0.003363123
Kurtosis-1.1032684
Mean2012.2258
Median Absolute Deviation (MAD)6
Skewness-0.074840197
Sum14900532
Variance45.797201
MonotonicityIncreasing
2024-06-24T12:16:48.587216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2000 324
 
4.4%
2014 324
 
4.4%
2023 324
 
4.4%
2022 324
 
4.4%
2021 324
 
4.4%
2020 324
 
4.4%
2019 324
 
4.4%
2018 324
 
4.4%
2017 324
 
4.4%
2016 324
 
4.4%
Other values (14) 4165
56.2%
ValueCountFrequency (%)
2000 324
4.4%
2001 216
2.9%
2003 324
4.4%
2004 304
4.1%
2005 324
4.4%
2006 324
4.4%
2007 324
4.4%
2008 324
4.4%
2009 324
4.4%
2010 324
4.4%
ValueCountFrequency (%)
2024 81
 
1.1%
2023 324
4.4%
2022 324
4.4%
2021 324
4.4%
2020 324
4.4%
2019 324
4.4%
2018 324
4.4%
2017 324
4.4%
2016 324
4.4%
2015 324
4.4%

Mês
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Fevereiro
639 
Março
639 
Janeiro
619 
Abril
612 
Maio
612 
Other values (7)
4284 

Length

Max length9
Median length8
Mean length6.421472
Min length4

Characters and Unicode

Total characters47551
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJaneiro
2nd rowJaneiro
3rd rowJaneiro
4th rowJaneiro
5th rowJaneiro

Common Values

ValueCountFrequency (%)
Fevereiro 639
8.6%
Março 639
8.6%
Janeiro 619
8.4%
Abril 612
8.3%
Maio 612
8.3%
Junho 612
8.3%
Julho 612
8.3%
Agosto 612
8.3%
Setembro 612
8.3%
Outubro 612
8.3%
Other values (2) 1224
16.5%

Length

2024-06-24T12:16:48.633149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fevereiro 639
8.6%
março 639
8.6%
janeiro 619
8.4%
abril 612
8.3%
maio 612
8.3%
junho 612
8.3%
julho 612
8.3%
agosto 612
8.3%
setembro 612
8.3%
outubro 612
8.3%
Other values (2) 1224
16.5%

Most occurring characters

ValueCountFrequency (%)
o 8017
16.9%
r 5596
11.8%
e 5596
11.8%
b 3060
 
6.4%
i 2482
 
5.2%
u 2448
 
5.1%
a 1870
 
3.9%
J 1843
 
3.9%
t 1836
 
3.9%
m 1836
 
3.9%
Other values (15) 12967
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 8017
16.9%
r 5596
11.8%
e 5596
11.8%
b 3060
 
6.4%
i 2482
 
5.2%
u 2448
 
5.1%
a 1870
 
3.9%
J 1843
 
3.9%
t 1836
 
3.9%
m 1836
 
3.9%
Other values (15) 12967
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 8017
16.9%
r 5596
11.8%
e 5596
11.8%
b 3060
 
6.4%
i 2482
 
5.2%
u 2448
 
5.1%
a 1870
 
3.9%
J 1843
 
3.9%
t 1836
 
3.9%
m 1836
 
3.9%
Other values (15) 12967
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 8017
16.9%
r 5596
11.8%
e 5596
11.8%
b 3060
 
6.4%
i 2482
 
5.2%
u 2448
 
5.1%
a 1870
 
3.9%
J 1843
 
3.9%
t 1836
 
3.9%
m 1836
 
3.9%
Other values (15) 12967
27.3%

UF
Categorical

UNIFORM 

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
AC
 
278
MA
 
278
GO
 
278
AM
 
278
AP
 
278
Other values (22)
6015 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14810
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAC
2nd rowAL
3rd rowAM
4th rowAP
5th rowBA

Common Values

ValueCountFrequency (%)
AC 278
 
3.8%
MA 278
 
3.8%
GO 278
 
3.8%
AM 278
 
3.8%
AP 278
 
3.8%
BA 278
 
3.8%
MG 278
 
3.8%
DF 278
 
3.8%
ES 278
 
3.8%
CE 278
 
3.8%
Other values (17) 4625
62.5%

Length

2024-06-24T12:16:48.700819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 278
 
3.8%
pa 278
 
3.8%
ms 278
 
3.8%
pr 278
 
3.8%
pi 278
 
3.8%
pe 278
 
3.8%
ma 278
 
3.8%
al 278
 
3.8%
mt 278
 
3.8%
pb 278
 
3.8%
Other values (17) 4625
62.5%

Most occurring characters

ValueCountFrequency (%)
A 1946
13.1%
P 1935
13.1%
R 1878
12.7%
S 1624
11.0%
M 1390
9.4%
E 1101
7.4%
C 823
 
5.6%
O 812
 
5.5%
G 556
 
3.8%
B 556
 
3.8%
Other values (7) 2189
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1946
13.1%
P 1935
13.1%
R 1878
12.7%
S 1624
11.0%
M 1390
9.4%
E 1101
7.4%
C 823
 
5.6%
O 812
 
5.5%
G 556
 
3.8%
B 556
 
3.8%
Other values (7) 2189
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1946
13.1%
P 1935
13.1%
R 1878
12.7%
S 1624
11.0%
M 1390
9.4%
E 1101
7.4%
C 823
 
5.6%
O 812
 
5.5%
G 556
 
3.8%
B 556
 
3.8%
Other values (7) 2189
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1946
13.1%
P 1935
13.1%
R 1878
12.7%
S 1624
11.0%
M 1390
9.4%
E 1101
7.4%
C 823
 
5.6%
O 812
 
5.5%
G 556
 
3.8%
B 556
 
3.8%
Other values (7) 2189
14.8%
Distinct7382
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90442421
Minimum-591595
Maximum2.7952883 × 109
Zeros20
Zeros (%)0.3%
Negative4
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:48.746589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-591595
5-th percentile3606.616
Q1191846
median5073299.8
Q362832349
95-th percentile4.0379662 × 108
Maximum2.7952883 × 109
Range2.7958799 × 109
Interquartile range (IQR)62640503

Descriptive statistics

Standard deviation2.6289687 × 108
Coefficient of variation (CV)2.9067872
Kurtosis32.449276
Mean90442421
Median Absolute Deviation (MAD)5069155.8
Skewness5.3085806
Sum6.6972613 × 1011
Variance6.9114766 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:48.798175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.3%
346754 2
 
< 0.1%
272 2
 
< 0.1%
94 2
 
< 0.1%
0.27 2
 
< 0.1%
80624455.01 1
 
< 0.1%
167342.98 1
 
< 0.1%
713788.2 1
 
< 0.1%
30093970.11 1
 
< 0.1%
231 1
 
< 0.1%
Other values (7372) 7372
99.6%
ValueCountFrequency (%)
-591595 1
 
< 0.1%
-179256.56 1
 
< 0.1%
-5357 1
 
< 0.1%
-3672 1
 
< 0.1%
0 20
0.3%
0.18 1
 
< 0.1%
0.24 1
 
< 0.1%
0.27 2
 
< 0.1%
0.35 1
 
< 0.1%
0.52 1
 
< 0.1%
ValueCountFrequency (%)
2795288310 1
< 0.1%
2576983664 1
< 0.1%
2486881748 1
< 0.1%
2448990126 1
< 0.1%
2432077264 1
< 0.1%
2418310416 1
< 0.1%
2416954418 1
< 0.1%
2404819436 1
< 0.1%
2365364831 1
< 0.1%
2352186885 1
< 0.1%

IMPOSTO SOBRE EXPORTAÇÃO
Real number (ℝ)

SKEWED  ZEROS 

Distinct5911
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676174.76
Minimum-3379762
Maximum1.2069438 × 109
Zeros767
Zeros (%)10.4%
Negative1770
Negative (%)23.9%
Memory size115.7 KiB
2024-06-24T12:16:48.853382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3379762
5-th percentile-253.664
Q10
median443.23
Q310257.95
95-th percentile441608.4
Maximum1.2069438 × 109
Range1.2103235 × 109
Interquartile range (IQR)10257.95

Descriptive statistics

Standard deviation22305627
Coefficient of variation (CV)32.987962
Kurtosis2029.0122
Mean676174.76
Median Absolute Deviation (MAD)623.43
Skewness43.952214
Sum5.0070741 × 109
Variance4.9754101 × 1014
MonotonicityNot monotonic
2024-06-24T12:16:48.911128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767
 
10.4%
10 31
 
0.4%
-1 20
 
0.3%
-2 18
 
0.2%
13 18
 
0.2%
12 17
 
0.2%
14 14
 
0.2%
15 13
 
0.2%
18 11
 
0.1%
17 10
 
0.1%
Other values (5901) 6486
87.6%
ValueCountFrequency (%)
-3379762 1
< 0.1%
-915595.88 1
< 0.1%
-843667 1
< 0.1%
-810594.46 1
< 0.1%
-509835 1
< 0.1%
-167979.53 1
< 0.1%
-153868.69 1
< 0.1%
-151357.41 1
< 0.1%
-138164.3 1
< 0.1%
-103932.27 1
< 0.1%
ValueCountFrequency (%)
1206943782 1
< 0.1%
917581499.6 1
< 0.1%
899717250.1 1
< 0.1%
701867577 1
< 0.1%
202101249.1 1
< 0.1%
133775906.7 1
< 0.1%
114082348.4 1
< 0.1%
99657204.88 1
< 0.1%
31327267.91 1
< 0.1%
30684772.7 1
< 0.1%

IPI - FUMO
Real number (ℝ)

ZEROS 

Distinct4361
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12572852
Minimum-43291858
Maximum8.9447763 × 108
Zeros2943
Zeros (%)39.7%
Negative6
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:48.968228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-43291858
5-th percentile0
Q10
median26031
Q31799103
95-th percentile60187352
Maximum8.9447763 × 108
Range9.3776949 × 108
Interquartile range (IQR)1799103

Descriptive statistics

Standard deviation53385439
Coefficient of variation (CV)4.2460882
Kurtosis53.151865
Mean12572852
Median Absolute Deviation (MAD)26031
Skewness6.5697858
Sum9.3101968 × 1010
Variance2.8500051 × 1015
MonotonicityNot monotonic
2024-06-24T12:16:49.021425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2943
39.7%
60.11 34
 
0.5%
320.69 12
 
0.2%
7.26 7
 
0.1%
6840 5
 
0.1%
100 5
 
0.1%
182.81 5
 
0.1%
4560 5
 
0.1%
840 4
 
0.1%
1140 4
 
0.1%
Other values (4351) 4381
59.2%
ValueCountFrequency (%)
-43291858.2 1
 
< 0.1%
-16632674 1
 
< 0.1%
-1023914 1
 
< 0.1%
-797349 1
 
< 0.1%
-680 1
 
< 0.1%
-393.71 1
 
< 0.1%
0 2943
39.7%
2.99 1
 
< 0.1%
7.26 7
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
894477627 1
< 0.1%
726381115 1
< 0.1%
660450316.7 1
< 0.1%
653634002 1
< 0.1%
575289369 1
< 0.1%
568819463.4 1
< 0.1%
564981441 1
< 0.1%
533130476.6 1
< 0.1%
521846903.4 1
< 0.1%
516544807.5 1
< 0.1%

IPI - BEBIDAS
Real number (ℝ)

ZEROS 

Distinct6799
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7843366.9
Minimum-10797231
Maximum1.4564511 × 108
Zeros604
Zeros (%)8.2%
Negative3
Negative (%)< 0.1%
Memory size115.7 KiB
2024-06-24T12:16:49.072766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-10797231
5-th percentile0
Q1444835.48
median2825576
Q38701250.5
95-th percentile32993859
Maximum1.4564511 × 108
Range1.5644234 × 108
Interquartile range (IQR)8256415

Descriptive statistics

Standard deviation14269879
Coefficient of variation (CV)1.8193563
Kurtosis18.196837
Mean7843366.9
Median Absolute Deviation (MAD)2767777.5
Skewness3.8148332
Sum5.8080132 × 1010
Variance2.0362944 × 1014
MonotonicityNot monotonic
2024-06-24T12:16:49.131576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 604
 
8.2%
780 2
 
< 0.1%
823 2
 
< 0.1%
3557 2
 
< 0.1%
58253.76 1
 
< 0.1%
501485.27 1
 
< 0.1%
15652503.83 1
 
< 0.1%
7729.88 1
 
< 0.1%
193223.33 1
 
< 0.1%
565139.58 1
 
< 0.1%
Other values (6789) 6789
91.7%
ValueCountFrequency (%)
-10797231 1
 
< 0.1%
-3797536 1
 
< 0.1%
-95.01 1
 
< 0.1%
0 604
8.2%
13.63 1
 
< 0.1%
20.4 1
 
< 0.1%
30.6 1
 
< 0.1%
33 1
 
< 0.1%
40.42 1
 
< 0.1%
61.18 1
 
< 0.1%
ValueCountFrequency (%)
145645112.6 1
< 0.1%
133996570 1
< 0.1%
133604881.2 1
< 0.1%
132778835.4 1
< 0.1%
125217654 1
< 0.1%
115006780.9 1
< 0.1%
112619720 1
< 0.1%
110193285.4 1
< 0.1%
108822729 1
< 0.1%
108105884 1
< 0.1%

IPI - AUTOMÓVEIS
Real number (ℝ)

ZEROS 

Distinct4104
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11911571
Minimum-5424603.3
Maximum3.4766857 × 108
Zeros3073
Zeros (%)41.5%
Negative7
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:49.187502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5424603.3
5-th percentile0
Q10
median3620
Q31393911
95-th percentile70899002
Maximum3.4766857 × 108
Range3.5309317 × 108
Interquartile range (IQR)1393911

Descriptive statistics

Standard deviation34938334
Coefficient of variation (CV)2.9331424
Kurtosis21.462581
Mean11911571
Median Absolute Deviation (MAD)3620
Skewness4.3281411
Sum8.8205181 × 1010
Variance1.2206871 × 1015
MonotonicityNot monotonic
2024-06-24T12:16:49.244830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3073
41.5%
155 24
 
0.3%
60.04 18
 
0.2%
115 17
 
0.2%
30 15
 
0.2%
71 14
 
0.2%
253.04 13
 
0.2%
25239.36 10
 
0.1%
6385.47 10
 
0.1%
75 10
 
0.1%
Other values (4094) 4201
56.7%
ValueCountFrequency (%)
-5424603.3 1
 
< 0.1%
-1454896.09 1
 
< 0.1%
-683502 1
 
< 0.1%
-538308.36 1
 
< 0.1%
-223003 1
 
< 0.1%
-6484 1
 
< 0.1%
-1977.37 1
 
< 0.1%
0 3073
41.5%
10 5
 
0.1%
13.3 1
 
< 0.1%
ValueCountFrequency (%)
347668565.9 1
< 0.1%
346669851.7 1
< 0.1%
288739237 1
< 0.1%
287028935.3 1
< 0.1%
276687114.8 1
< 0.1%
273868775.4 1
< 0.1%
272996515.7 1
< 0.1%
271900897.4 1
< 0.1%
268493463 1
< 0.1%
264567969.7 1
< 0.1%

IPI - VINCULADO À IMPORTACAO
Real number (ℝ)

ZEROS 

Distinct6817
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42375586
Minimum0
Maximum1.1521469 × 109
Zeros524
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2024-06-24T12:16:49.301241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1133013.76
median2017171
Q335159042
95-th percentile1.9362425 × 108
Maximum1.1521469 × 109
Range1.1521469 × 109
Interquartile range (IQR)35026028

Descriptive statistics

Standard deviation1.0619695 × 108
Coefficient of variation (CV)2.506088
Kurtosis26.199396
Mean42375586
Median Absolute Deviation (MAD)2017171
Skewness4.6132952
Sum3.1379121 × 1011
Variance1.1277792 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:49.358151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 524
 
7.1%
918.23 19
 
0.3%
12968.03 14
 
0.2%
4328.32 8
 
0.1%
437.99 6
 
0.1%
10 4
 
0.1%
2018.65 3
 
< 0.1%
98 2
 
< 0.1%
3136.04 2
 
< 0.1%
36410.18 2
 
< 0.1%
Other values (6807) 6821
92.1%
ValueCountFrequency (%)
0 524
7.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7.2 1
 
< 0.1%
8 1
 
< 0.1%
10 4
 
0.1%
11.14 1
 
< 0.1%
13 1
 
< 0.1%
14.16 1
 
< 0.1%
14.82 1
 
< 0.1%
ValueCountFrequency (%)
1152146874 1
< 0.1%
1091502050 1
< 0.1%
1071842066 1
< 0.1%
1033096749 1
< 0.1%
992679037.3 1
< 0.1%
981389621.3 1
< 0.1%
978509769.7 1
< 0.1%
975560711.8 1
< 0.1%
954352343.1 1
< 0.1%
948964700 1
< 0.1%

IPI - OUTROS
Real number (ℝ)

Distinct7403
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58700924
Minimum-3485777.1
Maximum1.6660526 × 109
Zeros1
Zeros (%)< 0.1%
Negative7
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:49.412932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3485777.1
5-th percentile21953.192
Q11488339
median4544201.6
Q344778576
95-th percentile2.0960997 × 108
Maximum1.6660526 × 109
Range1.6695384 × 109
Interquartile range (IQR)43290237

Descriptive statistics

Standard deviation1.6573651 × 108
Coefficient of variation (CV)2.8234056
Kurtosis27.943967
Mean58700924
Median Absolute Deviation (MAD)4421879.6
Skewness5.0391029
Sum4.3468034 × 1011
Variance2.7468592 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:49.466881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5572 2
 
< 0.1%
9050 2
 
< 0.1%
1558 1
 
< 0.1%
2594309.72 1
 
< 0.1%
914450922.1 1
 
< 0.1%
4951257.67 1
 
< 0.1%
118985776.6 1
 
< 0.1%
121591937.5 1
 
< 0.1%
17583.08 1
 
< 0.1%
1780247.63 1
 
< 0.1%
Other values (7393) 7393
99.8%
ValueCountFrequency (%)
-3485777.06 1
< 0.1%
-3264033 1
< 0.1%
-3067198.52 1
< 0.1%
-2291920.37 1
< 0.1%
-34206 1
< 0.1%
-7871 1
< 0.1%
-4169.42 1
< 0.1%
0 1
< 0.1%
312.1 1
< 0.1%
1006 1
< 0.1%
ValueCountFrequency (%)
1666052600 1
< 0.1%
1654772393 1
< 0.1%
1583899038 1
< 0.1%
1520479816 1
< 0.1%
1482288138 1
< 0.1%
1361877106 1
< 0.1%
1357775562 1
< 0.1%
1349707454 1
< 0.1%
1320541053 1
< 0.1%
1293717225 1
< 0.1%

IRPF
Real number (ℝ)

UNIQUE 

Distinct7405
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78203699
Minimum67719
Maximum5.2992448 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2024-06-24T12:16:49.521214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum67719
5-th percentile799091.2
Q14704077
median16179021
Q353251965
95-th percentile3.5232608 × 108
Maximum5.2992448 × 109
Range5.2991771 × 109
Interquartile range (IQR)48547888

Descriptive statistics

Standard deviation2.2769979 × 108
Coefficient of variation (CV)2.9116242
Kurtosis116.18524
Mean78203699
Median Absolute Deviation (MAD)13952539
Skewness8.5356109
Sum5.7909839 × 1011
Variance5.1847192 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:49.577095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177197 1
 
< 0.1%
778361350.7 1
 
< 0.1%
69551370.23 1
 
< 0.1%
158247692.7 1
 
< 0.1%
4198960.04 1
 
< 0.1%
11125549.97 1
 
< 0.1%
22525203.9 1
 
< 0.1%
325181969.8 1
 
< 0.1%
117564013 1
 
< 0.1%
16058129.81 1
 
< 0.1%
Other values (7395) 7395
99.9%
ValueCountFrequency (%)
67719 1
< 0.1%
69453 1
< 0.1%
80074 1
< 0.1%
84094 1
< 0.1%
87131 1
< 0.1%
94599 1
< 0.1%
114716 1
< 0.1%
121721 1
< 0.1%
124781 1
< 0.1%
126206 1
< 0.1%
ValueCountFrequency (%)
5299244801 1
< 0.1%
4806777728 1
< 0.1%
4083826684 1
< 0.1%
3459117027 1
< 0.1%
3197791235 1
< 0.1%
3168603820 1
< 0.1%
2942153033 1
< 0.1%
2719045366 1
< 0.1%
2622912827 1
< 0.1%
2385029848 1
< 0.1%

IRPJ - ENTIDADES FINANCEIRAS
Real number (ℝ)

ZEROS 

Distinct6727
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50112988
Minimum-8099952.9
Maximum7.7296189 × 109
Zeros614
Zeros (%)8.3%
Negative55
Negative (%)0.7%
Memory size115.7 KiB
2024-06-24T12:16:49.630952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-8099952.9
5-th percentile0
Q14815
median300673.43
Q39014015.7
95-th percentile2.4718369 × 108
Maximum7.7296189 × 109
Range7.7377188 × 109
Interquartile range (IQR)9009200.7

Descriptive statistics

Standard deviation2.3581287 × 108
Coefficient of variation (CV)4.7056239
Kurtosis247.21483
Mean50112988
Median Absolute Deviation (MAD)300673.43
Skewness12.280312
Sum3.7108667 × 1011
Variance5.5607711 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:49.690701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 614
 
8.3%
169 3
 
< 0.1%
1517 3
 
< 0.1%
77 3
 
< 0.1%
591 3
 
< 0.1%
123 3
 
< 0.1%
6129 2
 
< 0.1%
872 2
 
< 0.1%
2336 2
 
< 0.1%
8464 2
 
< 0.1%
Other values (6717) 6768
91.4%
ValueCountFrequency (%)
-8099952.92 1
< 0.1%
-3140682 1
< 0.1%
-2263798.08 1
< 0.1%
-768076.84 1
< 0.1%
-470737 1
< 0.1%
-306415.73 1
< 0.1%
-222111 1
< 0.1%
-201909 1
< 0.1%
-198388.82 1
< 0.1%
-194368.49 1
< 0.1%
ValueCountFrequency (%)
7729618874 1
< 0.1%
4866718247 1
< 0.1%
3965647816 1
< 0.1%
3894755872 1
< 0.1%
3866010419 1
< 0.1%
3747430136 1
< 0.1%
3632980142 1
< 0.1%
2968318005 1
< 0.1%
2939585625 1
< 0.1%
2837593862 1
< 0.1%

IRPJ - DEMAIS EMPRESAS
Real number (ℝ)

UNIQUE 

Distinct7405
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2900635 × 108
Minimum289754
Maximum2.2938669 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2024-06-24T12:16:49.745187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum289754
5-th percentile2256759.9
Q112637475
median47761359
Q32.011195 × 108
95-th percentile1.5482252 × 109
Maximum2.2938669 × 1010
Range2.2938379 × 1010
Interquartile range (IQR)1.8848202 × 108

Descriptive statistics

Standard deviation1.0455654 × 109
Coefficient of variation (CV)3.177949
Kurtosis96.403239
Mean3.2900635 × 108
Median Absolute Deviation (MAD)42762743
Skewness8.0893251
Sum2.436292 × 1012
Variance1.093207 × 1018
MonotonicityNot monotonic
2024-06-24T12:16:49.797285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676835 1
 
< 0.1%
2034879346 1
 
< 0.1%
232124187.2 1
 
< 0.1%
246782047.1 1
 
< 0.1%
4330911.61 1
 
< 0.1%
14091394.51 1
 
< 0.1%
23375983.03 1
 
< 0.1%
818483255.7 1
 
< 0.1%
284714061.8 1
 
< 0.1%
19209785.09 1
 
< 0.1%
Other values (7395) 7395
99.9%
ValueCountFrequency (%)
289754 1
< 0.1%
308073 1
< 0.1%
323340 1
< 0.1%
339398 1
< 0.1%
340255 1
< 0.1%
362135 1
< 0.1%
366799 1
< 0.1%
371442 1
< 0.1%
380610 1
< 0.1%
385853 1
< 0.1%
ValueCountFrequency (%)
2.293866899 × 10101
< 0.1%
1.83297883 × 10101
< 0.1%
1.658856326 × 10101
< 0.1%
1.586695916 × 10101
< 0.1%
1.47341015 × 10101
< 0.1%
1.375038614 × 10101
< 0.1%
1.3150664 × 10101
< 0.1%
1.2750939 × 10101
< 0.1%
1.224592339 × 10101
< 0.1%
1.209284469 × 10101
< 0.1%

IRRF - RENDIMENTOS DO TRABALHO
Real number (ℝ)

UNIQUE 

Distinct7405
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5925137 × 108
Minimum-74712501
Maximum9.7563497 × 109
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size115.7 KiB
2024-06-24T12:16:49.853522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-74712501
5-th percentile2801271.8
Q111493377
median37101128
Q31.2973872 × 108
95-th percentile1.4454519 × 109
Maximum9.7563497 × 109
Range9.8310622 × 109
Interquartile range (IQR)1.1824534 × 108

Descriptive statistics

Standard deviation6.9845723 × 108
Coefficient of variation (CV)2.6941313
Kurtosis30.431492
Mean2.5925137 × 108
Median Absolute Deviation (MAD)31239571
Skewness4.8708685
Sum1.9197564 × 1012
Variance4.8784251 × 1017
MonotonicityNot monotonic
2024-06-24T12:16:49.910824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1084940 1
 
< 0.1%
2396983442 1
 
< 0.1%
163518601 1
 
< 0.1%
302177770.6 1
 
< 0.1%
6282491.83 1
 
< 0.1%
17029124.7 1
 
< 0.1%
34594147.74 1
 
< 0.1%
1014179275 1
 
< 0.1%
261589800 1
 
< 0.1%
20989718.14 1
 
< 0.1%
Other values (7395) 7395
99.9%
ValueCountFrequency (%)
-74712501 1
< 0.1%
-13309526 1
< 0.1%
-130560 1
< 0.1%
228616 1
< 0.1%
231281 1
< 0.1%
244970 1
< 0.1%
313435 1
< 0.1%
324703 1
< 0.1%
325888 1
< 0.1%
344657 1
< 0.1%
ValueCountFrequency (%)
9756349736 1
< 0.1%
8648871034 1
< 0.1%
7928286209 1
< 0.1%
7309523966 1
< 0.1%
7059791297 1
< 0.1%
6900785699 1
< 0.1%
6680725125 1
< 0.1%
5805238472 1
< 0.1%
5692942112 1
< 0.1%
5614131188 1
< 0.1%
Distinct7403
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2859343 × 108
Minimum-1.8721384 × 108
Maximum1.4463375 × 1010
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:49.963765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.8721384 × 108
5-th percentile79539.908
Q1794282.11
median3726924
Q324609891
95-th percentile6.1352935 × 108
Maximum1.4463375 × 1010
Range1.4650589 × 1010
Interquartile range (IQR)23815609

Descriptive statistics

Standard deviation5.7218516 × 108
Coefficient of variation (CV)4.4495676
Kurtosis137.53805
Mean1.2859343 × 108
Median Absolute Deviation (MAD)3556138.4
Skewness9.722054
Sum9.5223436 × 1011
Variance3.2739586 × 1017
MonotonicityNot monotonic
2024-06-24T12:16:50.020519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13438 2
 
< 0.1%
16603 2
 
< 0.1%
27652 1
 
< 0.1%
2338234.12 1
 
< 0.1%
30162465.38 1
 
< 0.1%
76120903.66 1
 
< 0.1%
196981.57 1
 
< 0.1%
1795905.07 1
 
< 0.1%
1777802.32 1
 
< 0.1%
486571186.2 1
 
< 0.1%
Other values (7393) 7393
99.8%
ValueCountFrequency (%)
-187213840.3 1
< 0.1%
-24571449 1
< 0.1%
-16711966.76 1
< 0.1%
-5810771.01 1
< 0.1%
-1282364 1
< 0.1%
-1016583 1
< 0.1%
-988882 1
< 0.1%
-142198 1
< 0.1%
-80675 1
< 0.1%
3937 1
< 0.1%
ValueCountFrequency (%)
1.44633752 × 10101
< 0.1%
1.103565898 × 10101
< 0.1%
9357326591 1
< 0.1%
8928949421 1
< 0.1%
8596446922 1
< 0.1%
7966291318 1
< 0.1%
7381185557 1
< 0.1%
7214770855 1
< 0.1%
7153167541 1
< 0.1%
6945342239 1
< 0.1%

IRRF - REMESSAS P/ EXTERIOR
Real number (ℝ)

ZEROS 

Distinct7232
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64659141
Minimum-2.59046 × 108
Maximum6.713613 × 109
Zeros167
Zeros (%)2.3%
Negative30
Negative (%)0.4%
Memory size115.7 KiB
2024-06-24T12:16:50.076162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.59046 × 108
5-th percentile1350.4
Q1145524.25
median1650805.1
Q312969491
95-th percentile2.992217 × 108
Maximum6.713613 × 109
Range6.972659 × 109
Interquartile range (IQR)12823967

Descriptive statistics

Standard deviation3.0384997 × 108
Coefficient of variation (CV)4.6992578
Kurtosis128.3029
Mean64659141
Median Absolute Deviation (MAD)1646728.1
Skewness9.7133998
Sum4.7880094 × 1011
Variance9.2324804 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:50.133340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167
 
2.3%
3709 2
 
< 0.1%
50 2
 
< 0.1%
107 2
 
< 0.1%
100 2
 
< 0.1%
5719 2
 
< 0.1%
401 2
 
< 0.1%
325 2
 
< 0.1%
2424414.55 1
 
< 0.1%
960757.13 1
 
< 0.1%
Other values (7222) 7222
97.5%
ValueCountFrequency (%)
-259045996.4 1
< 0.1%
-22803113.7 1
< 0.1%
-1526698.73 1
< 0.1%
-715431 1
< 0.1%
-315331.3 1
< 0.1%
-267051 1
< 0.1%
-245886 1
< 0.1%
-127238 1
< 0.1%
-84114 1
< 0.1%
-69294 1
< 0.1%
ValueCountFrequency (%)
6713613018 1
< 0.1%
6145975184 1
< 0.1%
5207429452 1
< 0.1%
4899769770 1
< 0.1%
4846939055 1
< 0.1%
4818325178 1
< 0.1%
3906045956 1
< 0.1%
3826409099 1
< 0.1%
3741085873 1
< 0.1%
3392502290 1
< 0.1%

IRRF - OUTROS RENDIMENTOS
Real number (ℝ)

Distinct7403
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27172888
Minimum-2555620
Maximum9.3380086 × 108
Zeros2
Zeros (%)< 0.1%
Negative11
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:50.193981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2555620
5-th percentile126167.95
Q1897225
median3056655.4
Q311919649
95-th percentile1.3879222 × 108
Maximum9.3380086 × 108
Range9.3635648 × 108
Interquartile range (IQR)11022424

Descriptive statistics

Standard deviation82611965
Coefficient of variation (CV)3.0402351
Kurtosis37.532607
Mean27172888
Median Absolute Deviation (MAD)2709975.4
Skewness5.6057086
Sum2.0121523 × 1011
Variance6.8247368 × 1015
MonotonicityNot monotonic
2024-06-24T12:16:50.247242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36121 2
 
< 0.1%
0 2
 
< 0.1%
30894 1
 
< 0.1%
991965.69 1
 
< 0.1%
2005713.95 1
 
< 0.1%
15587574.11 1
 
< 0.1%
35049770.86 1
 
< 0.1%
210109.67 1
 
< 0.1%
602388.49 1
 
< 0.1%
1724945.35 1
 
< 0.1%
Other values (7393) 7393
99.8%
ValueCountFrequency (%)
-2555620 1
< 0.1%
-897777 1
< 0.1%
-820010 1
< 0.1%
-802388 1
< 0.1%
-411891 1
< 0.1%
-141905.21 1
< 0.1%
-82490.1 1
< 0.1%
-70488 1
< 0.1%
-32989 1
< 0.1%
-5223 1
< 0.1%
ValueCountFrequency (%)
933800860 1
< 0.1%
912120667.3 1
< 0.1%
854005675.6 1
< 0.1%
846955795.1 1
< 0.1%
820522199.4 1
< 0.1%
802941640.3 1
< 0.1%
799631949.1 1
< 0.1%
795127585.6 1
< 0.1%
791709092.9 1
< 0.1%
785582862.1 1
< 0.1%
Distinct7348
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82124864
Minimum-268901
Maximum3.7634486 × 109
Zeros37
Zeros (%)0.5%
Negative6
Negative (%)0.1%
Memory size115.7 KiB
2024-06-24T12:16:50.526782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-268901
5-th percentile10294.4
Q1238099.77
median2043926.2
Q312587458
95-th percentile3.6973822 × 108
Maximum3.7634486 × 109
Range3.7637175 × 109
Interquartile range (IQR)12349358

Descriptive statistics

Standard deviation3.4386167 × 108
Coefficient of variation (CV)4.1870593
Kurtosis48.711753
Mean82124864
Median Absolute Deviation (MAD)2006597.2
Skewness6.5479113
Sum6.0813462 × 1011
Variance1.1824085 × 1017
MonotonicityNot monotonic
2024-06-24T12:16:50.583935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
0.5%
2 4
 
0.1%
1 4
 
0.1%
200604.38 3
 
< 0.1%
6 3
 
< 0.1%
4 2
 
< 0.1%
35 2
 
< 0.1%
880 2
 
< 0.1%
949 2
 
< 0.1%
11953 2
 
< 0.1%
Other values (7338) 7344
99.2%
ValueCountFrequency (%)
-268901 1
 
< 0.1%
-264290 1
 
< 0.1%
-181961 1
 
< 0.1%
-123276 1
 
< 0.1%
-34299 1
 
< 0.1%
-242 1
 
< 0.1%
0 37
0.5%
0.73 1
 
< 0.1%
1 4
 
0.1%
1.27 1
 
< 0.1%
ValueCountFrequency (%)
3763448587 1
< 0.1%
3752569213 1
< 0.1%
3714614551 1
< 0.1%
3694651441 1
< 0.1%
3649459694 1
< 0.1%
3647427369 1
< 0.1%
3644651338 1
< 0.1%
3559174044 1
< 0.1%
3544200799 1
< 0.1%
3507794694 1
< 0.1%

IMPOSTO TERRITORIAL RURAL
Real number (ℝ)

Distinct7400
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3256393.4
Minimum-17756226
Maximum3.7514995 × 108
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size115.7 KiB
2024-06-24T12:16:50.639152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-17756226
5-th percentile22078.142
Q187348.5
median305548.93
Q31180282
95-th percentile11429337
Maximum3.7514995 × 108
Range3.9290617 × 108
Interquartile range (IQR)1092933.5

Descriptive statistics

Standard deviation16346013
Coefficient of variation (CV)5.0196677
Kurtosis166.11667
Mean3256393.4
Median Absolute Deviation (MAD)260837.52
Skewness11.55588
Sum2.4113593 × 1010
Variance2.6719213 × 1014
MonotonicityNot monotonic
2024-06-24T12:16:50.696335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20272 2
 
< 0.1%
34926 2
 
< 0.1%
59130.44 2
 
< 0.1%
1257459 2
 
< 0.1%
45571 2
 
< 0.1%
28447 1
 
< 0.1%
110038.84 1
 
< 0.1%
3002619.13 1
 
< 0.1%
354475.99 1
 
< 0.1%
86217.59 1
 
< 0.1%
Other values (7390) 7390
99.8%
ValueCountFrequency (%)
-17756225.7 1
< 0.1%
-378931.78 1
< 0.1%
1034 1
< 0.1%
1261 1
< 0.1%
1471 1
< 0.1%
1735 1
< 0.1%
1862 1
< 0.1%
1948 1
< 0.1%
2142 1
< 0.1%
2432 1
< 0.1%
ValueCountFrequency (%)
375149946.2 1
< 0.1%
326509783.3 1
< 0.1%
278534088.6 1
< 0.1%
278195244.6 1
< 0.1%
265854569.2 1
< 0.1%
265208783.9 1
< 0.1%
257050943.5 1
< 0.1%
241187976 1
< 0.1%
240635534.8 1
< 0.1%
234379972.6 1
< 0.1%

CPMF
Real number (ℝ)

ZEROS 

Distinct3465
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22708378
Minimum-3496452
Maximum2.332493 × 109
Zeros3904
Zeros (%)52.7%
Negative21
Negative (%)0.3%
Memory size115.7 KiB
2024-06-24T12:16:50.749940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3496452
5-th percentile0
Q10
median0
Q3120796
95-th percentile29366538
Maximum2.332493 × 109
Range2.3359895 × 109
Interquartile range (IQR)120796

Descriptive statistics

Standard deviation1.5574414 × 108
Coefficient of variation (CV)6.8584439
Kurtosis89.543672
Mean22708378
Median Absolute Deviation (MAD)0
Skewness9.0487173
Sum1.6815554 × 1011
Variance2.4256236 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:50.804551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3904
52.7%
3 5
 
0.1%
2 5
 
0.1%
13 4
 
0.1%
1 4
 
0.1%
11 3
 
< 0.1%
4 3
 
< 0.1%
4.4 2
 
< 0.1%
3.55 2
 
< 0.1%
2.22 2
 
< 0.1%
Other values (3455) 3471
46.9%
ValueCountFrequency (%)
-3496452 1
< 0.1%
-689387.77 1
< 0.1%
-539556.07 1
< 0.1%
-462872.33 1
< 0.1%
-93831 1
< 0.1%
-19349 1
< 0.1%
-6724.97 1
< 0.1%
-6246.26 1
< 0.1%
-4589 1
< 0.1%
-4159.58 1
< 0.1%
ValueCountFrequency (%)
2332493003 1
< 0.1%
2115947222 1
< 0.1%
1963922063 1
< 0.1%
1955364442 1
< 0.1%
1954329648 1
< 0.1%
1951592433 1
< 0.1%
1919379762 1
< 0.1%
1906244544 1
< 0.1%
1891848620 1
< 0.1%
1886036977 1
< 0.1%

COFINS
Real number (ℝ)

ZEROS 

Distinct865
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13870757
Minimum0
Maximum2.2990327 × 109
Zeros6541
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2024-06-24T12:16:50.854665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile44282787
Maximum2.2990327 × 109
Range2.2990327 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.06352 × 108
Coefficient of variation (CV)7.6673538
Kurtosis218.95912
Mean13870757
Median Absolute Deviation (MAD)0
Skewness13.762957
Sum1.0271296 × 1011
Variance1.1310749 × 1016
MonotonicityNot monotonic
2024-06-24T12:16:50.908632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6541
88.3%
12497742 1
 
< 0.1%
77483367 1
 
< 0.1%
82672759 1
 
< 0.1%
41700784 1
 
< 0.1%
207662579 1
 
< 0.1%
39593451 1
 
< 0.1%
44259039 1
 
< 0.1%
10927270 1
 
< 0.1%
16987432 1
 
< 0.1%
Other values (855) 855
 
11.5%
ValueCountFrequency (%)
0 6541
88.3%
1049745 1
 
< 0.1%
1104880 1
 
< 0.1%
1182289 1
 
< 0.1%
1184166 1
 
< 0.1%
1235857 1
 
< 0.1%
1245292 1
 
< 0.1%
1264476 1
 
< 0.1%
1265317 1
 
< 0.1%
1276739 1
 
< 0.1%
ValueCountFrequency (%)
2299032749 1
< 0.1%
2172645500 1
< 0.1%
2082164034 1
< 0.1%
1982544199 1
< 0.1%
1963930514 1
< 0.1%
1854137053 1
< 0.1%
1824938828 1
< 0.1%
1798997045 1
< 0.1%
1766307245 1
< 0.1%
1742924592 1
< 0.1%

TOTAL_ARRECADADO
Real number (ℝ)

UNIQUE 

Distinct7405
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2834832 × 109
Minimum-4598430
Maximum5.9264196 × 1010
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size115.7 KiB
2024-06-24T12:16:50.959359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-4598430
5-th percentile9853920.7
Q145032179
median1.7614687 × 108
Q37.5663996 × 108
95-th percentile5.9467391 × 109
Maximum5.9264196 × 1010
Range5.9268794 × 1010
Interquartile range (IQR)7.1160778 × 108

Descriptive statistics

Standard deviation3.6326802 × 109
Coefficient of variation (CV)2.8303295
Kurtosis47.918177
Mean1.2834832 × 109
Median Absolute Deviation (MAD)1.5735777 × 108
Skewness6.0213843
Sum9.5041928 × 1012
Variance1.3196366 × 1019
MonotonicityNot monotonic
2024-06-24T12:16:51.013767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4093028 1
 
< 0.1%
1.400689386 × 10101
 
< 0.1%
1211339783 1
 
< 0.1%
1342850050 1
 
< 0.1%
15345504.7 1
 
< 0.1%
49076551.1 1
 
< 0.1%
91218919.42 1
 
< 0.1%
3778946911 1
 
< 0.1%
1428463844 1
 
< 0.1%
62322140.23 1
 
< 0.1%
Other values (7395) 7395
99.9%
ValueCountFrequency (%)
-4598430 1
< 0.1%
1448301 1
< 0.1%
2248717 1
< 0.1%
2527280 1
< 0.1%
2593022 1
< 0.1%
2643737 1
< 0.1%
2688890 1
< 0.1%
2782447 1
< 0.1%
2833090 1
< 0.1%
2885882 1
< 0.1%
ValueCountFrequency (%)
5.926419576 × 10101
< 0.1%
4.977299346 × 10101
< 0.1%
4.343079737 × 10101
< 0.1%
4.241576987 × 10101
< 0.1%
3.975068897 × 10101
< 0.1%
3.872979259 × 10101
< 0.1%
3.795893113 × 10101
< 0.1%
3.661483248 × 10101
< 0.1%
3.622393744 × 10101
< 0.1%
3.609743056 × 10101
< 0.1%

Interactions

2024-06-24T12:16:47.483367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.152265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.111029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.001094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.891387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.655987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.582802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.403615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.325350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.137479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.932439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.889306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.665211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.486818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.315270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.290457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.088484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.925310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.750123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.697770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.521200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.208666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.149846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.041805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.928015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.695189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.623362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.442239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.365188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.175874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.973551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.927003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.704199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.526385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.354741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.328060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.128378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.965395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.786259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.733547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.561157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.293270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.190238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.082087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.966195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.734652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.664726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.481894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.404850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.233708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.015555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.965907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.744345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.567187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.395081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.367258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.169680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.005354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.823267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.788384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.600939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.359875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.229922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.122498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.006390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.775753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.706959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.522881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.446588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.273187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.058545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.006639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.783811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.608708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.591440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.407190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.210729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.048210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.861749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.829902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.638197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.468878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.266920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.160458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.041002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.813526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.744981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.560614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.483848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.309427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.097361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.042094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.820416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.648790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.628882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.442331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.251796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.086819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.898136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.865537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.679798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.509816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.308548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.201938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.081177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.853996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.787229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.602871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-24T12:16:37.014757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.935751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.740961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.682823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.472728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.287038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.107770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.087901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.895622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.718605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.546646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.509873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.290249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:48.124431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.954889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.841182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.659520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.507487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.404170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.244134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.058918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.980006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.781706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.727557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.513705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.330151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.152484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.130056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.936811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.761283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.589836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.549948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.332085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:48.167212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:31.997503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.883616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.701719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.548095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.446913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.286036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.102517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.021133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.821492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.770557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.553748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.371463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.196669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.173453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.977667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.805649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.631890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.590039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.374099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:48.204269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.035791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.923561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.811860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.582100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.486154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.323510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.140210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.059709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.857228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.809544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.590001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.407778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.234649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.211934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.012311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.843763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.671154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.623352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.408880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:48.240821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.071162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:32.960915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:33.850695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:34.616738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:35.543618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:36.361792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:37.178253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.097189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:38.893972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:39.848595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:40.625325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:41.445438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:42.273607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:43.249947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.049890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:44.881568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:45.708461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:46.658797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-24T12:16:47.443980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-24T12:16:48.306911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-24T12:16:48.432047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AnoMêsUFIMPOSTO SOBRE IMPORTAÇÃOIMPOSTO SOBRE EXPORTAÇÃOIPI - FUMOIPI - BEBIDASIPI - AUTOMÓVEISIPI - VINCULADO À IMPORTACAOIPI - OUTROSIRPFIRPJ - ENTIDADES FINANCEIRASIRPJ - DEMAIS EMPRESASIRRF - RENDIMENTOS DO TRABALHOIRRF - RENDIMENTOS DO CAPITALIRRF - REMESSAS P/ EXTERIORIRRF - OUTROS RENDIMENTOSIMPOSTO S/ OPERAÇÕES FINANCEIRASIMPOSTO TERRITORIAL RURALCPMFCOFINSTOTAL_ARRECADADO
02000JaneiroAC231.00.0292096.00.00.0167.01558.0177197.0643.0676835.01084940.027652.0570.030894.01220.028447.0103.01770475.04093028.0
12000JaneiroAL475088.033873.01329338.0812470.00.0141735.03676847.0460804.0561.02672106.04454947.0544056.0191382.0309573.026939.04252539.097248.07493570.026973076.0
22000JaneiroAM11679405.00.01507146.01791471.027796.04414483.01800346.0700623.090828.010058713.010174718.01241708.03580022.01029164.0843881.040171.0832562.044000981.093814018.0
32000JaneiroAP24267.00.0424862.01419.0321.022333.012165.0139226.044135.0586572.01444486.0205031.05825.034602.06999.06477.00.01682102.04640822.0
42000JaneiroBA10476457.013.05542123.011533707.00.07496476.023743858.02498647.0177378.025695696.027277025.013374802.02302766.02022200.01218485.02446535.011923442.081027229.0228756839.0
52000JaneiroCE5576921.00.04535653.03148254.00.01734529.03716458.01971500.059874.013247447.015940490.06311419.0948600.01941958.0745659.0114393.04547426.036892799.0101433380.0
62000JaneiroDF2254341.043.03197500.02777942.00.02079272.02433339.03067374.03913461.071582680.0257391352.0124316928.07145075.035581549.022472279.064578.0240726401.0141910257.0920914371.0
72000JaneiroES31950799.00.03598736.020408.01503039.026840430.04036463.01620843.02542.029105465.09380778.010515327.04717282.01703727.0491261.01563052.028681.049157907.0176236740.0
82000JaneiroGO3747911.00.03236092.05064141.01091185.0986314.02654994.01401385.045754.018823998.013094463.06275215.0132749.01043647.0638840.02269467.03912150.047986002.0112404307.0
92000JaneiroMA1714749.069.01124738.04061569.00.0206691.0484436.0479170.04832.03251185.04149532.0781569.01130.0289120.086071.0593391.0612313.09654439.027495004.0
AnoMêsUFIMPOSTO SOBRE IMPORTAÇÃOIMPOSTO SOBRE EXPORTAÇÃOIPI - FUMOIPI - BEBIDASIPI - AUTOMÓVEISIPI - VINCULADO À IMPORTACAOIPI - OUTROSIRPFIRPJ - ENTIDADES FINANCEIRASIRPJ - DEMAIS EMPRESASIRRF - RENDIMENTOS DO TRABALHOIRRF - RENDIMENTOS DO CAPITALIRRF - REMESSAS P/ EXTERIORIRRF - OUTROS RENDIMENTOSIMPOSTO S/ OPERAÇÕES FINANCEIRASIMPOSTO TERRITORIAL RURALCPMFCOFINSTOTAL_ARRECADADO
78472024MarçoPI1.080297e+07-1.800.000000e+001710526.750.000000e+004.311223e+062.129538e+061.310838e+071.115296e+044.492228e+073.768966e+071.758334e+062.981880e+064.110460e+062.214084e+05965411.200.00.01.247232e+08
78482024MarçoRJ2.265810e+0818741.065.774848e+0510266135.403.083134e+071.012351e+081.230349e+082.230489e+082.579138e+084.913295e+092.388072e+092.632400e+096.509999e+081.581961e+082.913355e+081158120.570.00.01.200896e+10
78492024MarçoRN6.531258e+06-3.057.260000e+00418137.215.431790e+032.491648e+062.535647e+061.449881e+076.778428e+055.258138e+076.143646e+074.437379e+062.109778e+063.804874e+061.456330e+06187905.940.00.01.531729e+08
78502024MarçoRS1.198907e+082338.781.623147e+0817229136.821.669087e+071.150825e+082.306029e+081.420642e+083.163137e+076.183760e+087.526882e+081.632696e+087.807834e+075.847474e+072.025337e+083581625.900.00.02.712511e+09
78512024MarçoRO4.259459e+07-92.430.000000e+0015936.013.236959e+041.686069e+077.335857e+068.798839e+064.372943e+052.520413e+073.613082e+076.921385e+063.214756e+051.361129e+061.714452e+07886899.630.00.01.640458e+08
78522024MarçoRR4.176311e+040.000.000000e+000.000.000000e+001.313218e+048.405307e+042.931498e+063.858670e+031.035559e+071.296915e+073.032121e+052.007814e+048.286758e+051.256391e+05115602.220.00.02.779224e+07
78532024MarçoSC1.145658e+09-2721.898.276000e+0110385020.783.971773e+074.199974e+083.143088e+089.483545e+071.801126e+077.018191e+084.223393e+089.516803e+075.891456e+073.185204e+078.297521e+07951322.260.00.03.436930e+09
78542024MarçoSP1.778419e+0938203.152.386842e+0651120764.982.273373e+085.498449e+081.233497e+097.106990e+081.527903e+097.063112e+097.309524e+096.538369e+092.754760e+098.540057e+083.544201e+0918847271.310.00.03.416406e+10
78552024MarçoSE5.666047e+061872.383.309714e+051262320.150.000000e+001.161892e+068.561310e+061.039469e+072.975598e+063.226274e+073.525751e+074.082837e+061.676212e+063.330496e+065.288795e+0677990.460.00.01.123313e+08
78562024MarçoTO4.609480e+06-0.010.000000e+002070.370.000000e+001.609433e+068.335072e+056.547910e+061.875666e+044.392847e+072.059676e+077.263769e+058.403451e+051.262365e+064.524017e+051476992.190.00.08.290487e+07